Static, rule-based surveillance triggers thousands of false positives daily, overwhelming compliance teams while missing novel collusion patterns and AI-driven spoofing. Legacy systems operate on known signatures, not behavioral intent.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Traditional rule-based systems cannot adapt to novel, sophisticated manipulation tactics, creating regulatory and financial exposure.
Static, rule-based surveillance triggers thousands of false positives daily, overwhelming compliance teams while missing novel collusion patterns and AI-driven spoofing. Legacy systems operate on known signatures, not behavioral intent.
Modern market abuse is a dynamic, multi-agent game. Detecting it requires simulation, not just static filtering.
Inference Systems builds deterministic surveillance AI using pattern recognition and multi-agent simulation. We deploy models that learn the tactics of abuse, not just the historical artifacts, providing real-time detection with a >60% reduction in false positives. Explore our broader capabilities in Financial Services Algorithmic AI and Risk Modeling or see how we ensure compliance through Agentic AI for Financial Compliance.
Our Market Manipulation Pattern Recognition systems are engineered to deliver measurable business value, directly protecting your revenue from abuse and ensuring robust compliance with global market regulations like MAR and MiFID II.
Deploy AI surveillance that identifies complex market abuse patterns—including spoofing, layering, and quote stuffing—in real-time, enabling immediate intervention to prevent losses and protect market integrity.
Automatically generate detailed, timestamped audit trails and suspicious activity reports (SARs) aligned with ESMA, FCA, and SEC requirements. Our systems ensure your surveillance evidence is structured, searchable, and defensible.
Leverage graph neural networks and contextual analysis to drastically reduce false positive alerts compared to legacy rule-based systems. Focus your compliance team's effort on genuine high-risk events, not noise.
A structured, milestone-driven approach to deploying a real-time surveillance AI system, ensuring regulatory compliance and operational integration at each phase.
| Phase & Key Deliverables | Timeline | Core Activities | Outcome & Handoff |
|---|---|---|---|
Phase 1: Discovery & Pattern Definition | 2-3 weeks | Regulatory framework analysis, historical abuse data review, initial spoofing/layering pattern library definition. | Technical specification document and approved pattern detection logic for PoC. |
Phase 2: Proof-of-Concept (PoC) Development | 4-6 weeks | Build core detection engine (e.g., using graph networks), test on historical tick data, validate against known cases. | Functional PoC demonstrating >85% recall on historical data; go/no-go decision for MVP. |
Phase 3: MVP Development & Back-Testing | 6-8 weeks | Develop production-ready detection models, integrate with market data feed, conduct rigorous back-testing and adversarial simulation. | Deployable MVP with audited performance metrics and integration blueprint for your infrastructure. |
Phase 4: Pilot Integration & Live Monitoring | 3-4 weeks | Deploy in isolated production environment, connect to live data, establish alerting dashboard, train compliance team. | System live in monitoring mode; compliance team trained; initial live detection report. |
Phase 5: Full Production & Scale | Ongoing | Scale to full market coverage, implement continuous model retraining loop, integrate with case management systems. | Fully operational system with 99.9% uptime SLA, generating automated alerts and audit trails. |
Ongoing Support & Model Governance | Post-deployment | Monthly performance reviews, model drift monitoring, quarterly pattern library updates based on emerging tactics. | Guaranteed system accuracy and compliance with evolving market abuse regulations (e.g., MiFID II). |
We engineer surveillance systems with a focus on deterministic outcomes, verifiable accuracy, and seamless integration into your existing market data and compliance infrastructure.
We deploy unsupervised learning agents to simulate normal market behavior and flag statistical outliers. This detects novel, evolving manipulation tactics not present in the historical library by analyzing order book dynamics and cross-asset correlations.
Alerts are generated based on configurable, rule-based thresholds combining pattern matches and anomaly scores. Every alert is tagged with the specific logic and data points that triggered it, ensuring full auditability for compliance teams and regulators.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers about deploying AI surveillance to detect spoofing, layering, and wash trading in real-time.
Typical deployment for a real-time detection system is 4-8 weeks. This includes 2 weeks for data pipeline integration, 2-3 weeks for model fine-tuning on your historical order book data, and 1-2 weeks for system integration and validation. For complex multi-asset class deployments, timelines extend to 10-12 weeks. We provide a detailed project plan within the first week of engagement.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
